In data storage networks, latency is the amount of time required or used by a storage device to respond to or service a data request, such as an Input/Output (I/O) request. A higher or longer than usual latency time indicates a performance degradation on the data storage network experienced by the application using the data storage network. Conventional data storage network performance evaluation processes measure latency times to determine if the data storage network is experiencing performance degradation. However, conventional latency-based performance evaluation methods have shown to be highly inaccurate, as data storage network performance is impacted by a plurality of factors other than just latency times. More particularly, data storage network performance is known to depend on various characteristics of the application workload of the data storage network, such as the size of I/O requests, CPU saturation, port saturation, disk saturation, queue depth, and cache misses, for example. Therefore, conventional latency-based performance evaluation methods for data storage networks are prone to yield inaccurate results and falsely indicate performance degradation. Thus, a challenge for data storage networks and administrators thereof is how to accurately identify if there is a performance issue in the storage environment, and if a performance issue is identified, the likely cause of the performance issue. Another challenge with conventional data storage networks is identifying the root cause of latency-based performance degradation, as conventional latency-based performance evaluation techniques are not capable of analyzing or otherwise determining factors that may be causing latency issues. Further, conventional latency-based network performance evaluation methods are not able to offer insight into upcoming network latency issues, which would be desirable for network administrators in managing network activity.